Review on AI-Driven Innovations in Stroke Care: Enhancing Diagnostic Accuracy, Treatment Efficacy, and Rehabilitation Outcomes
Muhammad Subhan *
Department of Medicine (Gastroenterology), Allama Iqbal Medical College Lahore/Jinnah Hospital, Lahore, Pakistan.
Shaji Faisal
Gandhi Medical College & Hospital, Secunderabad, India.
Muhammad Usman Khan
Department of Neurology, King Edward Medical University, Lahore, Pakistan.
Ernette Espiegle
Faculty of Medicine and Pharmacy of the University of State of Haiti, Haiti.
Muhammad Waqas
Internal Medicine, Jinnah Sindh Medical University, Pakistan.
Ruqiya Bibi
Allama Iqbal Medical College Lahore/Jinnah Hospital Lahore, Pakistan.
Muhammad Farooq Haider
Jiujiang University, China, Services Hospital Lahore, Pakistan.
Ganesh Pendli
PES Institute of Medical Science and Research, Chittoor, India.
Salman Kazmi
Department of Internal Medicine, South Medical Ward, Mayo Hospital Lahore, Pakistan.
Iqra Yaseen Khan
King Edward Medical University Lahore, Pakistan.
*Author to whom correspondence should be addressed.
Abstract
Stroke remains one of the leading causes of both disability and mortality worldwide, requiring immediate intervention to limit brain damage and prevent complications. Integrating artificial intelligence (AI) into stroke management has revolutionized diagnostic, treatment, and rehabilitation processes, offering new opportunities for improving precision and outcomes. This study investigates the current tools, applications, and challenges associated with AI-assisted decision support systems in stroke management to enhance diagnostic accuracy, treatment efficacy, and personalized care. Through an extensive review, we analyzed how AI plays a pivotal role in stroke care, including diagnostic imaging, treatment decision-making, and rehabilitation. AI demonstrated remarkable accuracy in MRI and CT stroke detection, significantly improving diagnostic efficiency. AI-powered decision support systems optimized treatment plans, particularly in selecting candidates for thrombolysis and mechanical thrombectomy, thereby reducing mortality and improving outcomes. AI-driven rehabilitation programs provide personalized therapy, enhancing motor recovery and patient outcomes. Despite its potential, challenges such as data heterogeneity, privacy concerns, and the need for large, diverse datasets remain significant barriers. Overall, AI has proven to be transformative in stroke care, streamlining diagnostic, treatment, and rehabilitation processes. Its continued advancement may further refine predictive models and create more effective, tailored healthcare interventions globally.
Keywords: Stroke, AI-driven innovations, diagnostic accuracy, chronic disability